


'''simple docstring'''


def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Tuple							= current_set.copy()
				for row_index, row in enumerate(SCREAMING_SNAKE_CASE__	):
								_SCREAMING_SNAKE_CASE					:			Dict							= row[0]
								for column_index, column in enumerate(SCREAMING_SNAKE_CASE__	):
												if magnitude == 0:
																_SCREAMING_SNAKE_CASE					:			Optional[Any]							= column
																continue
												_SCREAMING_SNAKE_CASE					:			Any							= column / magnitude
    # Subtract to cancel term
				_SCREAMING_SNAKE_CASE					:			Dict							= current_set[0]
				_SCREAMING_SNAKE_CASE					:			Dict							= [first_row]
				_SCREAMING_SNAKE_CASE					:			Optional[int]							= current_set[1::]
				for row in current_set:
								_SCREAMING_SNAKE_CASE					:			List[Any]							= []
								# If first term is 0, it is already in form we want, so we preserve it
								if row[0] == 0:
												final_set.append(SCREAMING_SNAKE_CASE__	)
												continue
								for column_index in range(len(SCREAMING_SNAKE_CASE__	)	):
												temp_row.append(first_row[column_index] - row[column_index]	)
								final_set.append(SCREAMING_SNAKE_CASE__	)
				# Create next recursion iteration set
				if len(final_set[0]	) != 3:
								_SCREAMING_SNAKE_CASE					:			List[Any]							= final_set[0]
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= []
								_SCREAMING_SNAKE_CASE					:			List[Any]							= []
								for row in final_set[1::]:
												current_first_column.append(row[0]	)
												next_iteration.append(row[1::]	)
								_SCREAMING_SNAKE_CASE					:			int							= simplify(SCREAMING_SNAKE_CASE__	)
								for i in range(len(SCREAMING_SNAKE_CASE__	)	):
												resultant[i].insert(0				, current_first_column[i]	)
								resultant.insert(0				, SCREAMING_SNAKE_CASE__	)
								_SCREAMING_SNAKE_CASE					:			int							= resultant
				return final_set







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				if len(SCREAMING_SNAKE_CASE__	) == 0:
								raise IndexError("""solve_simultaneous() requires n lists of length n+1"""	)
				_SCREAMING_SNAKE_CASE					:			Dict							= len(SCREAMING_SNAKE_CASE__	) + 1
				if any(len(SCREAMING_SNAKE_CASE__	) != _length for item in equations	):
								raise IndexError("""solve_simultaneous() requires n lists of length n+1"""	)
				for row in equations:
								if any(not isinstance(SCREAMING_SNAKE_CASE__				, (int, float)	) for column in row	):
												raise ValueError("""solve_simultaneous() requires lists of integers"""	)
				if len(SCREAMING_SNAKE_CASE__	) == 1:
								return [equations[0][-1] / equations[0][0]]
				_SCREAMING_SNAKE_CASE					:			Dict							= equations.copy()
				if any(0 in row for row in data_set	):
								_SCREAMING_SNAKE_CASE					:			int							= data_set.copy()
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= []
								for row_index, row in enumerate(SCREAMING_SNAKE_CASE__	):
												if 0 not in row:
																_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= data_set.pop(SCREAMING_SNAKE_CASE__	)
																break
								if not full_row:
												raise ValueError("""solve_simultaneous() requires at least 1 full equation"""	)
								data_set.insert(0				, SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= data_set.copy()
				_SCREAMING_SNAKE_CASE					:			List[Any]							= simplify(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			str							= simplified[::-1]
				_SCREAMING_SNAKE_CASE					:			list							= []
				for row in simplified:
								_SCREAMING_SNAKE_CASE					:			List[Any]							= row[-1]
								if not solutions:
												if row[-2] == 0:
																solutions.append(0	)
																continue
												solutions.append(current_solution / row[-2]	)
												continue
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= row.copy()[: len(SCREAMING_SNAKE_CASE__	) - 1 :]
								while temp_row[0] == 0:
												temp_row.pop(0	)
								if len(SCREAMING_SNAKE_CASE__	) == 0:
												solutions.append(0	)
												continue
								_SCREAMING_SNAKE_CASE					:			str							= temp_row[1::]
								_SCREAMING_SNAKE_CASE					:			Dict							= temp_row[::-1]
								for column_index, column in enumerate(SCREAMING_SNAKE_CASE__	):
												current_solution -= column * solutions[column_index]
								solutions.append(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			List[str]							= []
				for item in solutions:
								final.append(float(round(SCREAMING_SNAKE_CASE__				, 5	)	)	)
				return final[::-1]


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				UpperCAmelCase_						:  List[str]				  =						[
				    [2, 1, 1, 1, 1, 4],
				    [1, 2, 1, 1, 1, 5],
				    [1, 1, 2, 1, 1, 6],
				    [1, 1, 1, 2, 1, 7],
				    [1, 1, 1, 1, 2, 8],
				]
				print(solve_simultaneous(eq))
				print(solve_simultaneous([[4, 2]]))








'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_						:  int				  =						{'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_						:  Tuple				  =						[
				    'WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'WavLMForAudioFrameClassification',
				    'WavLMForCTC',
				    'WavLMForSequenceClassification',
				    'WavLMForXVector',
				    'WavLMModel',
				    'WavLMPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_wavlm import (
								    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    WavLMForAudioFrameClassification,
								    WavLMForCTC,
								    WavLMForSequenceClassification,
								    WavLMForXVector,
								    WavLMModel,
								    WavLMPreTrainedModel,
								)

else:
				import sys

				UpperCAmelCase_						:  Any				  =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




